Current methods of predicting user entertainment and/or content favorites can be complicated, time-consuming and generate inaccurate results. Given the increasing volume and availability of programming content, it has become remarkably difficult to efficiently process and identify meaningful content for a consumer. The flexibility offered by content recommendation and classification systems can be beneficial, as such, there remains an ever-present need for improved and simplified ways of processing and identifying content similarities to alleviate processing burdens and maximize computing resources for content recommendation systems.